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    Forward-Backward smoothing for hidden markov models of point pattern data

    Access Status
    Fulltext not available
    Authors
    Dam, N.
    Phung, D.
    Vo, Ba-Ngu
    Huynh, V.
    Date
    2018
    Type
    Conference Paper
    
    Metadata
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    Citation
    Dam, N. and Phung, D. and Vo, B. and Huynh, V. 2018. Forward-Backward smoothing for hidden markov models of point pattern data, pp. 252-261.
    Source Title
    Proceedings - 2017 International Conference on Data Science and Advanced Analytics, DSAA 2017
    DOI
    10.1109/DSAA.2017.78
    ISBN
    9781509050048
    School
    School of Electrical Engineering, Computing and Mathematical Science (EECMS)
    Funding and Sponsorship
    http://purl.org/au-research/grants/arc/DP160104662
    URI
    http://hdl.handle.net/20.500.11937/66625
    Collection
    • Curtin Research Publications
    Abstract

    © 2017 IEEE. This paper considers a discrete-time sequential latent model for point pattern data, specifically a hidden Markov model (HMM) where each observation is an instantiation of a random finite set (RFS). This so-called RFS-HMM is worthy of investigation since point pattern data are ubiquitous in artificial intelligence and data science. We address the three basic problems typically encountered in such a sequential latent model, namely likelihood computation, hidden state inference, and parameter estimation. Moreover, we develop algorithms for solving these problems including forward-backward smoothing for likelihood computation and hidden state inference, and expectation-maximisation for parameter estimation. Simulation studies are used to demonstrate key properties of RFS-HMM, whilst real data in the domain of human dynamics are used to demonstrate its applicability.

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